Montreal cognitive assessment test versus Phototest for diagnostic of cognitive impairment in older adults
Bibliographic record
Abstract
Introduction: cognitive impairment constitutes one of the most important socio-health challenges presented in society, it is characterized by a deterioration of cognitive functions such as language, memory, attention, thinking and calculation; given its epidemiology, instruments have been implemented for early diagnosis such as: the Montreal Cognitive Assessment Test and the Fototest.Aim: to describe in a comparative way the internal validity of the MoCA test and Fototest for the diagnosis of cognitive impairment in older adults by means of bibliographic compilation. Methodology: narrative literature review using a systematic search, obtaining information from databases such as Pubmed, Science Direct and Scopus. With the help of search terms such as "Cognitive dysfunction" AND "Fototest"; Cognitive dysfunction" AND "Montreal".Results: with the proposed research, 6 articles were found and analyzed, finding studies with higher sensitivity 0,94 and specificity 0,60 in favor of the Montreal assessment test for the diagnosis of cognitive impairment with respect to the Phototest. However, not enough studies were found for the Phototest, although, the latter presented advantages in application time and does not discriminate educational level.Conclusions: in this review it was found that cognitive impairment in older adults is influenced mainly by age and educational level, taking into account the collection of information it was clearly evidenced that older adults with low educational level present lower cognitive development and higher risk of suffering cognitive impairment
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".